{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:54:39Z","timestamp":1774659279282,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819500291","type":"print"},{"value":"9789819500307","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-0030-7_3","type":"book-chapter","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T07:35:25Z","timestamp":1753342525000},"page":"28-39","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Universal Periodicity Injection Module for Crystal Property Prediction"],"prefix":"10.1007","author":[{"given":"Yichao","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangde","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lai","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Te","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/S0962492900000015","volume":"9","author":"MD Buhmann","year":"2000","unstructured":"Buhmann, M.D.: Radial basis functions. Acta numerica 9, 1\u201338 (2000)","journal-title":"Acta numerica"},{"issue":"5","key":"3_CR2","doi-asserted-by":"publisher","first-page":"2958","DOI":"10.1021\/jacs.2c11420","volume":"145","author":"Z Cao","year":"2023","unstructured":"Cao, Z., Magar, R., Wang, Y., Barati Farimani, A.: Moformer: self-supervised transformer model for metal\u2013organic framework property prediction. J. Am. Chem. Soc. 145(5), 2958\u20132967 (2023)","journal-title":"J. Am. Chem. Soc."},{"issue":"1","key":"3_CR3","first-page":"1525","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai, T., Draxler, R.R.: Root mean square error (rmse) or mean absolute error (mae). Geosci. Model Dev. Disc. 7(1), 1525\u20131534 (2014)","journal-title":"Geosci. Model Dev. Disc."},{"issue":"9","key":"3_CR4","doi-asserted-by":"publisher","first-page":"3564","DOI":"10.1021\/acs.chemmater.9b01294","volume":"31","author":"C Chen","year":"2019","unstructured":"Chen, C., Ye, W., Zuo, Y., Zheng, C., Ong, S.P.: Graph networks as a universal machine learning framework for molecules and crystals. Chem. Mater. 31(9), 3564\u20133572 (2019)","journal-title":"Chem. Mater."},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Choudhary, K., DeCost, B.: Atomistic line graph neural network for improved materials property predictions. npj Comput. Mater. 7(1), 185 (2021)","DOI":"10.1038\/s41524-021-00650-1"},{"issue":"8","key":"3_CR6","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevMaterials.2.083801","volume":"2","author":"K Choudhary","year":"2018","unstructured":"Choudhary, K., DeCost, B., Tavazza, F.: Machine learning with force-field-inspired descriptors for materials: fast screening and mapping energy landscape. Phys. Rev. Mater. 2(8), 083801 (2018)","journal-title":"Phys. Rev. Mater."},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Choudhary, K., et al.: The joint automated repository for various integrated simulations (jarvis) for data-driven materials design. npj Comput. Mater. 6(1), 173 (2020)","DOI":"10.1038\/s41524-020-00440-1"},{"key":"3_CR8","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Jha, D., et al.: Elemnet: deep learning the chemistry of materials from only elemental composition. Sci. Rep. 8(1), 17593 (2018)","DOI":"10.1038\/s41598-018-35934-y"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Jha, D., et al.: Irnet: a general purpose deep residual regression framework for materials discovery. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2385\u20132393 (2019)","DOI":"10.1145\/3292500.3330703"},{"key":"3_CR11","volume-title":"Introduction to Solid State Physics","author":"C Kittel","year":"1996","unstructured":"Kittel, C., McEuen, P., McEuen, P.: Introduction to Solid State Physics, vol. 8. Wiley, New York (1996)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"LeSar, R.: Introduction to Computational Materials Science: Fundamentals to Applications. Cambridge University Press, Cambridge (2013)","DOI":"10.1017\/CBO9781139033398"},{"issue":"12","key":"3_CR13","doi-asserted-by":"publisher","first-page":"6827","DOI":"10.1007\/s00521-024-09432-4","volume":"36","author":"K Liu","year":"2024","unstructured":"Liu, K., Yang, K., Gao, S.: A periodicity aware transformer for crystal property prediction. Neural Comput. Appl. 36(12), 6827\u20136838 (2024)","journal-title":"Neural Comput. Appl."},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Liu, K., Yang, K., Zhang, J., Xu, R.: S2snet: A pretrained neural network for superconductivity discovery. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. pp. 5101\u20135107. International Joint Conferences on Artificial Intelligence Organization (7 2022). https:\/\/doi.org\/10.24963\/ijcai.2022\/708, aI for Good","DOI":"10.24963\/ijcai.2022\/708"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Liu, L., Liu, X., Gao, J., Chen, W., Han, J.: Understanding the difficulty of training transformers. arXiv preprint arXiv:2004.08249 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.463"},{"issue":"32","key":"3_CR16","doi-asserted-by":"publisher","first-page":"18141","DOI":"10.1039\/D0CP01474E","volume":"22","author":"SY Louis","year":"2020","unstructured":"Louis, S.Y., et al.: Graph convolutional neural networks with global attention for improved materials property prediction. Phys. Chem. Chem. Phys. 22(32), 18141\u201318148 (2020)","journal-title":"Phys. Chem. Chem. Phys."},{"issue":"11","key":"3_CR17","doi-asserted-by":"publisher","first-page":"4392","DOI":"10.1021\/acs.jcim.3c02070","volume":"64","author":"KD Luong","year":"2024","unstructured":"Luong, K.D., Singh, A.: Application of transformers in cheminformatics. J. Chem. Inf. Model. 64(11), 4392\u20134409 (2024)","journal-title":"J. Chem. Inf. Model."},{"issue":"4","key":"3_CR18","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1039\/D2DD00018K","volume":"1","author":"E Ren","year":"2022","unstructured":"Ren, E., Guilbaud, P., Coudert, F.X.: High-throughput computational screening of nanoporous materials in targeted applications. Digital Disc. 1(4), 355\u2013374 (2022)","journal-title":"Digital Disc."},{"issue":"1","key":"3_CR19","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1146\/annurev-biodatasci-122120-124216","volume":"5","author":"R Rodr\u00edguez-P\u00e9rez","year":"2022","unstructured":"Rodr\u00edguez-P\u00e9rez, R., Miljkovi\u0107, F., Bajorath, J.: Machine learning in chemoinformatics and medicinal chemistry. Ann. Rev. Biomed. Data Sci. 5(1), 43\u201365 (2022)","journal-title":"Ann. Rev. Biomed. Data Sci."},{"issue":"12","key":"3_CR20","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1002\/jcc.25787","volume":"40","author":"AS Rosen","year":"2019","unstructured":"Rosen, A.S., Notestein, J.M., Snurr, R.Q.: Identifying promising metal\u2013organic frameworks for heterogeneous catalysis via high-throughput periodic density functional theory. J. Comput. Chem. 40(12), 1305\u20131318 (2019)","journal-title":"J. Comput. Chem."},{"key":"3_CR21","unstructured":"Sch\u00fctt, K., et al.: Schnet: a continuous-filter convolutional neural network for modeling quantum interactions. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"3_CR22","unstructured":"Shen, S., Liu, K., Zhu, M., Chen, H.: Boost your crystal model with denoising pre-training. In: ICML 2024 AI for Science Workshop (2024)"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Stanev, V., et al.: Machine learning modeling of superconducting critical temperature. npj Comput. Mater. 4(1), 1\u201314 (2018)","DOI":"10.1038\/s41524-018-0085-8"},{"key":"3_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"4","key":"3_CR25","doi-asserted-by":"publisher","first-page":"2345","DOI":"10.1103\/PhysRevB.37.2345","volume":"37","author":"P Villars","year":"1988","unstructured":"Villars, P., Phillips, J.C.: Quantum structural diagrams and high-t c superconductivity. Phys. Rev. B 37(4), 2345 (1988)","journal-title":"Phys. Rev. B"},{"issue":"1","key":"3_CR26","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1038\/s41524-021-00545-1","volume":"7","author":"AYT Wang","year":"2021","unstructured":"Wang, A.Y.T., Kauwe, S.K., Murdock, R.J., Sparks, T.D.: Compositionally restricted attention-based network for materials property predictions. NPJ Comput. Mater. 7(1), 77 (2021)","journal-title":"NPJ Comput. Mater."},{"key":"3_CR27","unstructured":"Xie, T., Fu, X., Ganea, O.E., Barzilay, R., Jaakkola, T.: Crystal diffusion variational autoencoder for periodic material generation. arXiv preprint arXiv:2110.06197 (2021)"},{"issue":"14","key":"3_CR28","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.120.145301","volume":"120","author":"T Xie","year":"2018","unstructured":"Xie, T., Grossman, J.C.: Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120(14), 145301 (2018)","journal-title":"Phys. Rev. Lett."},{"key":"3_CR29","first-page":"15066","volume":"35","author":"K Yan","year":"2022","unstructured":"Yan, K., Liu, Y., Lin, Y., Ji, S.: Periodic graph transformers for crystal material property prediction. Adv. Neural. Inf. Process. Syst. 35, 15066\u201315080 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jia, J., Koltun, V.: Exploring self-attention for image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10076\u201310085 (2020)","DOI":"10.1109\/CVPR42600.2020.01009"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0030-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:11:00Z","timestamp":1774656660000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0030-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500291","9789819500307"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0030-7_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}